Biology subjects

Hoinka, J.

Publications and source records attributed to Hoinka, J..

7 recordsLinked to original sources

bioRxiv2026

Combined inference of known and novel mutational signatures with ReDeNovo

Mutational signatures represent characteristic mutational patterns imprinted on the genome by mutagenic processes. They can provide information about the impact of the environmental and endogenous cellular processes on tumor mutations and can suggest treatment. Analysis of presence and strength of mutational signatures in cancer genomes has become a cornerstone in analysis of new and legacy cancer data. However, a precise inference of novel (de novo) signatures requires a large set of genomes, and methods focusing on estimating the presence of previously defined signatures are unable to uncover potential novel signatures that might emerge in new data. Thus, reliable methods to address these challenges are needed. We formally define the Combined Mutational Signature Inference Problem (CMSI) for the identification of known signatures and the inference of novel signatures in cancer data. CMSI represents non-convex optimization, and we provide a heuristic algorithm, ReDeNovo, to solve it efficiently. We extensively validated ReDeNovo on simulated data, evaluating its ability to precisely estimate presence and exposure to known signatures and to discover of novel signatures. On both tasks ReDeNovo outperformed existing approaches. In real biological data, ReDeNovo identified signatures missed by previous analyses and defined a new signature related to UV light exposure. ReDeNovo method provides a new and powerful tool to uncover mutational signatures. ReDeNovo is available from https://github.com/ncbi/redenovo.

genomics↗

bioRxiv2026

Toward identification of common DNA repair process in mutational signatures

Mutational signatures are characteristic patterns of mutation frequencies assumed to be generated by specific mutagenic processes. A growing catalog of mutational signatures exists, but tools to systematically infer relationships between them remain limited. A mutational signature can be viewed as the combined outcome of two processes: DNA damage and DNA repair. Since cancer therapies often target DNA repair, inferring DNA repair pathways is important for treatment design, even when the mutagenic process is unknown. Here, we model the DNA repair step as a transformation, called RePrint, from damaged nucleotides to repair-related mutation patterns conditioned on the damage. We demonstrate that RePrint similarity is indicative of shared DNA repair mechanisms, enabling guilt-by-association prediction of DNA repair pathways. Using experimentally annotated signatures from environmental exposures and CRISPR gene knockouts as gold standards, we demonstrate that RePrint-based clustering consistently outperforms signature-based clustering across multiple evaluation metrics. We validate several guilt-by-association predictions with literature evidence, demonstrating RePrints ability to identify shared repair mechanisms even among signatures with divergent mutational profiles. RePrint provides the first approach to systematically transfer DNA repair information between signatures, opening doors to understanding signatures of unknown origin and informing therapeutic strategies. An open-source implementation is available at https://github.com/wojtowicz-lab/RePrintPy

cancer biology↗

bioRxiv2025

Connecting spatial regions to clinical phenotypes by transferring knowledge from bulk patient data

Spatially resolved transcriptomics (SRT) technology has enabled a new level of knowledge about tumors. Many critical tumor properties, such as invasiveness and growth, depend on both specific transcriptomic changes in tumor cells and the tumor microenvironment. However, computational methods to study clinical phenotypes of spatial regions, such as hazard and drug response, have not yet been developed. Since clinical phenotypes are measured at the patient level and not at the level of spatial regions, such a method would require transferring knowledge from the patient level domain to the spot level domain. To overcome this challenge, we developed SpacePhenotyper. Our approach uses algebraic spectral techniques to transfer the predictive relationship between gene expression and clinical phenotypes from bulk gene expression data to SRT data. Our approach captures a gene expression pattern that is predictive of the phenotype of interest in the form of a vector, the "Eigen-Patient," which is then used to quantify the phenotype in spatial spots. After extensively validating SpacePhenotyper on simulated and real data, we utilize it to study how the spatial heterogeneity of breast cancer tumors influences residual cancer burden after treatment. By assigning relative quantities of clinical phenotypes to spatial locations, SpacePhenotyper has proven a powerful tool for the identification and interpretation of transcriptional changes over spatial regions and of spatially-regulated patterns of cellular states. SpacePhenotyper is implemented in Python. The source code and data sets used for and generated during this study are available at https://github.com/ncbi/SpacePhenotyper

cancer biology↗

bioRxiv2020

RepairSig: Deconvolution of DNA damage and repaircontributions to the mutational landscape of cancer

Many mutagenic processes leave characteristic imprints on cancer genomes known as mutational signatures. These signatures have been of recent interest regarding their applicability in studying processes shaping the mutational landscape of cancer. In particular, pinpointing the presence of altered DNA repair pathways can have important therapeutic implications. However, mutational signatures of DNA repair deficiencies are often hard to infer. This challenge emerges as a result of deficient DNA repair processes acting by modifying the outcome of other mutagens. Thus, they exhibit non-additive effects that are not depicted by the current paradigm for modeling mutational processes as independent signatures. To close this gap, we present RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW, a method that accounts for interactions between DNA damage and repair and is able to uncover unbiased signatures of deficient DNA repair processes. In particular, RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW was able to replace three MMR deficiency signatures previously proposed to be active in breast cancer, with just one signature strikingly similar to the experimentally derived signature. As the first method to model interactions between mutagenic processes, RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW is an important step towards biologically more realistic modeling of mutational processes in cancer. The source code for RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW is publicly available at https://github.com/ncbi/RepairSig.

bioinformatics↗

bioRxiv2018

Accurate sub-population detection and mapping across single cell experiments with PopCorn

The identification of sub-populations of cells present in a sample, and the comparison of such sub-populations across samples are among the most frequently performed analyzes of single cell data. Current tools for these kind of data however fall short in their ability to adequately perform these tasks. We introduce a novel method, PopCorn (single cell sub-Populations Comparison), allowing for the identification of sub-populations of cells present within individual experiments while simultaneously performing sub-populations mapping across these experiments. PopCorn utilizes several novel algorithmic solutions enabling the execution of these tasks with unprecedented precision. As such, PopCorn provides a much needed tool for comparative analysis of populations of single-cells.

bioinformatics↗

bioRxiv2018

Novel analysis of HT-SELEX data elucidates the role of DNA shape in transcription factor binding

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the question if in the absence of any sequence similarity to the binding motif, DNA shape can still increase probability of binding was yet to be addressed.\n\nTo address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allowed us to detect evidence for the role of DNA shape features in TF binding.\n\nOur approach revealed that, even in the absence of the sequence motif, TFs have propensity to weakly bind to DNA molecules enriched in specific shape features. Surprisingly, we also found that some properties of DNA shape contribute to promiscuous binding of all tested TF families. Strikingly, such promiscuously bound shapes correspond to the most frequent shape formed by the DNA. We propose that this promiscuous binding facilitates diffusing of TFs along the DNA molecule before it is locked in its binding site.

bioinformatics↗

bioRxiv2017

AptaBlocks: Accelerating the Design of RNA-based Drug Delivery Systems

Synthetic RNA molecules are increasingly used to alter cellular functions. These successful applications indicate that RNA-based therapeutics might be able to target currently undruggable genes. However, to achieve this promise, an effective method for delivering therapeutic RNAs into specific cells is required. Recently, RNA aptamers emerged as promising delivery agents due to their ability of binding specific cell receptors. Crucially, these aptamers can frequently be internalized into the cells expressing these receptors on their surfaces. This property is leveraged in aptamer based drug delivery systems by combining such receptor-specific aptamers with a therapeutic \"cargo\" such that the aptamer facilitates the internalization of the cargo into the cell. The advancement of this technology however is contingent on an efficient method to produce stable molecular complexes that include specific aptamers and cargoes. A recently proposed experimental procedure for obtaining such complexes relies on conjugating the aptamer and the cargo with complementary RNA strands so that when such modified molecules are incubated together, the complementary RNA strands hybridize to form a double-stranded \"sticky bridge\" connecting the aptamer with its cargo. However, designing appropriate sticky bridge sequences guaranteeing the formation and stability of the complex while simultaneously not interfering with the aptamer or the cargo as well as not causing spurious aggregation of the molecules during incubation has proven highly challenging. To fill this gap, we developed AptaBlocks, a computational method to design sticky bridges to connect RNA-based molecules (blocks). AptaBlocks relies on a biophysically inspired theoretical model capturing the complex objectives of the design and yet is simple enough to allow for efficient parameter estimation. Given this model, the sticky bridge sequence is optimized using a Monte Carlo algorithm based on heat-bath transitions. The effectiveness of the algorithm has been verified computationally and experimentally. AptaBlocks can be used in variety of experimental settings and its preliminary version has already been leveraged to design an aptamer based delivery system for a cytotoxic drug targeting Pancreatic ductal adenocarcinoma cells. It is thus expected that AptaBlocks will play a substantial role in accelerating RNA-based drug delivery design.\n\nAptaBlocks is available at https://github.com/wyjhxq/AptaBlocks.

bioinformatics↗